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DOE OSTI2023

Nonequilibrium effects on stability of hybrid stars with first-order phase transitions

The stability of hybrid stars with first-order phase transitions as determined by calculating fundamental radial oscillation modes is known to differ from the predictions of the widely used Bardeen-Thorne-Meltzer criterion. We consider the effects of out-of-chemical-equilibrium physics on the radial modes and hence stability of these objects. For a barotropic equation of state, this is done by allowing the adiabatic sound speed to differ from the equilibrium sound speed. We show that doing so extends the stable branches of stellar models, allowing stars with rapid phase transitions to support stable higher-order stellar multiplets similarly to stars with multiple slow phase transitions. We also derive a new junction condition to impose on the oscillation modes at the phase transition. Termed the reactive condition, it is physically motivated, consistent with the generalized junction conditions between two phases, and has the common rapid and slow conditions as limiting cases. Unlike the two common cases, it can only be applied to nonbarotropic stars. Here, we apply this junction condition to hybrid stellar models generated using a two-phase equation of state consisting of nuclear matter with unpaired quark matter at high densities joined by a first-order phase transition and show that, like in the slow limiting case, stars that are classically unstable are stabilized by a finite chemical reaction speed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Analysis of Differential Financial Impacts of LWR Load-Following Operations. Revision 1

This document reports the application of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to a case study conducted in collaboration with Arizona Public Service (APS). The study is a work in progress; this report presents a description of current models and corresponding results for a particular consideration. APS is anticipating several operational challenges. The first is rapid growth of Variable Renewable Energy (VRE) sources such as wind and solar power providing energy to the grid in the APS service region. This creates challenges for baseload operation of the Palo Verde Generating Station (PVGS), a nuclear power plant co-owned and operated by APS. The second challenge for APS is a change in the cooling water acquisition contract with the Sub Regional Operating Group (SROG). The existing contract is expiring soon, and a renewal is only available for a significantly higher price of water. These two challenges represent a difficulty in maintaining profitability for PVGS as local area demand is highly fluctuant and water prices may increase. Furthermore, excess production at PVGS is currently traded on the Palo Verde energy Hub (PVH), which occasionally offers very low or negative locational marginal prices (LMP) for electricity. This analysis considers the potential benefits of occasional curtailment of PVGS as a method to mitigate unprofitable production hours. Similar to load following, optional daily curtailment would allow APS to elect curtailment of PVGS as low as 70% of its nominal operating level between the hours of 08:00 and 16:00 provided local area demand is met and it is profitable to do so. This document reports a stochastic differential analysis of the Net Present Value (NPV) of PVGS as operated by NPS as a result of optional curtailment operation. To model this differential case, HERON has been designed from previous efforts in this study as a generic tool to accelerate the design and execution of RAVEN workflows for technoeconomic analysis of grid energy systems. HERON leverages both RAVEN and the CashFlow plugin for RAVEN to stochastically explore or optimize a response surface with energy grid system production capacities as inputs and mean NPV as a goal function. HERON performs these evaluations by stochastically sampling market, weather, and demand events using RAVEN’s ARMA reduced-order model capability to generate boundary conditions for dispatch optimization. The algorithm performs the dispatch optimization for each of the synthetic scenarios. For this case, HERON designs workflows to simulate behavior of the PVH, wind, solar, and APS local area demand, and use these synthetic scenarios to stochastically evaluate the differential benefit of optional curtailment for each scenario. While this study finds optional curtailment to be a potential source of revenue, the magnitude of the differential NPV is sufficiently small that it is unlikely a priority for increasing the profitability of PVGS. Further, the differential NPV shrinks throughout the simulated years, suggesting the long-term benefit of such operation given current assumptions is negligible.

14 SOLAR ENERGY↗

DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗

NASA NTRS1977

The effect of ambient temperature and humidity on the carbon monoxide emissions of an idling gas turbine

Changes in ambient temperature and humidity affect the exhaust emissions of a gas turbine engine. The results of a test program employing a JT8D combustor are presented which quantize the effect of these changes on carbon monoxide emissions at simulated idle operating conditions. Analytical results generated by a kinetic model of the combustion process and reflecting changing ambient conditions are given. It is shown that for a complete range of possible ambient variations, significant changes do occur in the amount of carbon monoxide emitted by a gas turbine engine.

Kauffman, C. W.↗

NASA NTRS1979

A structural model of the adaptive human pilot

A compensatory tracking model of the human pilot is offered which attempts to provide a more realistic representation of the human's signal processing structure than that which is exhibited by pilot models currently in use. Two features of the model distinguish it from other representations of the human pilot. First, proprioceptive information from the control stick or manipulator constitutes one of the major feedback paths in the model, providing feedback of vehicle output rate due to control activity. Implicit in this feedback loop is a model of the vehicle dynamics which is valid in and beyond the region of crossover. Second, error-rate information is continuously derived and independently but intermittently controlled. An output injected remnant model is offered and qualitatively justified on the basis of providing a measure of the effect of inaccuracies such as time variations in the pilot's internal model of the controlled element dynamics. The data from experimental tracking tasks involving five different controlled element dynamics and one nonideal viewing condition were matched with model generated describing functions and remnant power spectral densities.

Hess, R. A.↗

NASA NTRS2003

Simulation Model Development for Icing Effects Flight Training

A high-fidelity simulation model for icing effects flight training was developed from wind tunnel data for the DeHavilland DHC-6 Twin Otter aircraft. First, a flight model of the un-iced airplane was developed and then modifications were generated to model the icing conditions. The models were validated against data records from the NASA Twin Otter Icing Research flight test program with only minimal refinements being required. The goals of this program were to demonstrate the effectiveness of such a simulator for training pilots to recognize and recover from icing situations and to establish a process for modeling icing effects to be used for future training devices.

Barnhart, Billy P.↗

NASA NTRS1994

Solution of the Chapman-Ferraro problem with an arbitrary magnetopause

We present a global model of the magnetic field of the magnetosphere that includes the effects of the Chapman-Ferraro currents at the magnetopause. In contrast to ealier models, the magnetopause shape is arbitrary, thus allowing the use of more realistic geometries. The internal magnetospheric field model of Hilmer and Voigt (1993), is completely shielded within the magnetopause by solving the Laplace equation with Neumann boundary conditions using a finite difference method on a non-orthogonal, curvilinear grid. The resulting model magnetosphere is perfectly closed although the method can also be applied with more general boundary conditions, to generate a set of open models based on the approach of Toffoletto and Hill (1989, 1993). The purpose of this paper is to demonstrate the feasibility of a purely numerical approach to solving the Chapman-Ferraro problem with arbitrary magnetopause shape and boundary conditions.

Toffoletto, F. R.↗

DOE OSTI2025

Long-Term Evaluation of Remedial Technology Performance in the Laboratory: Multi-year Experimental Test Plan

An understanding of the long-term effectiveness of remediation technologies is central to sustainable environmental cleanup. Long-term experiments are valuable for reducing uncertainty and predicting remediation outcomes at scales required for regulatory compliance. However, these types of tests can be costly and challenging to interpret. Therefore, contaminated sites often rely on short-term laboratory experiments that may not account for the potentially significant effects of gradual, time- dependent processes governing contaminant retention, release, and species transformation. For example, short-term lab experiments (from months to a year) conducted with sediments from the unsaturated and saturated zones at the Hanford Site play an important role in initial evaluations of remediation technologies but cannot capture the full extent of time-dependent reactions, leaving uncertainties in field-scale deployment. Here, long-term testing will be conducted on select technologies based on their performance in short-term testing. The overall objective is to directly address the challenges described above by generating and analyzing data on long-term (2–10 years) efficacy of selected remediation technologies, integrating this understanding into models, and providing critical input for remediation planning, monitoring, and 5-year review cycles for field-implemented remedies. Specific objectives include: 1. Evaluating long-term efficiency of promising remedies under site-specific conditions. 2. Generating robust parameters for modeling, reducing uncertainty in predictive simulations. 3. Advancing integrated monitoring by combining geochemical and geophysical observations. 4. Informing field-scale implementation by incrementally advancing technologies, identifying failure mechanisms early, and prioritizing robust, cost-effective remedies. Through systematic evaluation of technologies in laboratory-scale column experiments, integrated monitoring, and modeling support, this project is designed to bolster confidence in the long-term robustness of selected technologies. The ultimate outcome is the identification and deployment of more reliable, cost-effective remedies that safeguard human health and the environment while reducing the uncertainties that have historically hindered cleanup progress at Hanford Site. An experimental approach was developed and initiated for long-term testing potentially up to 10 years. The table below summarizes the experimental approach developed for testing select technologies and presented in this multi-year experimental test plan.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning techniques have previously successfully reconstructed unobserved regions of flow in canonical fluid mechanics problems and two-dimensional geophysical flows, but these techniques have not yet been demonstrated in the three-dimensional atmospheric boundary layer. Here, we conduct a numerical analogue of a field campaign with spatially limited measurements using large-eddy simulation. We pose flow reconstruction as an inpainting problem, and reconstruct realistic samples of turbulent, three-dimensional flow with the use of a latent diffusion model. The diffusion model generates physically plausible turbulent structures on larger spatial scales, even when input observations cover less than 1% of the volume. Through a combination of qualitative visualization and quantitative assessment, we demonstrate that the diffusion model generates meaningfully diverse samples when conditioned on just one observation. These samples successfully serve as initial conditions for a large-eddy simulation code. We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.

17 WIND ENERGY↗

DOE OSTI2024

Integrated modeling for assessing climate change impacts on water resources and hydropower potential in the Himalayas

Regional hydroclimatic variability and change can affect water resources and hydropower generation. It is essential to assess hydropower potential under current and future climatic conditions to inform the design and operation of hydropower infrastructures. Here, we employ an integrated modeling framework to assess the impact of projected hydroclimatic conditions on water resource systems and hydropower generation. The integrated framework samples climate model outputs under different scenarios to force a hydrologic model and produces streamflow projections. The projected streamflows are inputs for the future hydropower potential assessment. We implement the framework in the central Himalayan river basin. Our results demonstrate substantial spatiotemporal variability in different water balance components (precipitation, evapotranspiration, and water yield) under current and future climatic conditions. For the Himalayan Tila river basin, the annual average energy production is expected to increase under future hydroclimatic conditions (up to 39% in Tila-2 hydropower project, suggested by ensemble mean). Furthermore, this increase in energy is driven mainly by the increased streamflow projections, particularly during the dry season and in the late century. Our results highlight the impacts of hydroclimatic variability in hydropower productions and are of practical use to provide decision-relevant information for designing and operating hydropower infrastructures. The integrated modeling framework presented here is region-specific; however, the approach is reproducible, and the overall insights are generalizable across the Himalayan region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Availability Assessment Based Case-Sensitive Power System Restoration Strategy

Increasingly frequent severe weather events in recent years threaten the security of power systems and result in major power outages throughout the world. The development of reasonable power system restoration (PSR) solutions is therefore urgently needed to speed up the recovery of the power supply while at the same time steering clear of vulnerable and risky equipment. This paper aims to develop a case-sensitive PSR model for power transmission systems that can adjust restoration solutions according to the evaluated availability of outage equipment in specific blackout scenarios and weather conditions. A novel PSR model that integrates the startup of generating units, formulation of the restoration network, renewable energy sources, and availability assessment of devices is proposed. A reformulated model is also proposed to relieve the computational burden of complex PSR problems. The availability of outage equipment is comprehensively assessed based on historical operating records, fault diagnosis results, and weather conditions. The assessed availability results are sensitive to the characteristics of real blackout cases and will support system operators generate case-sensitive PSR solutions while mitigating the vulnerable equipment. The feasibility and effectiveness of the proposed PSR model and its reformulations are verified through case studies.

availability assessment↗

DOE OSTI2016

Thermal–hydraulic–mechanical modeling of a large-scale heater test to investigate rock salt and crushed salt behavior under repository conditions for heat-generating nuclear waste

The Thermal Simulation for Drift Emplacement heater test is modeled with two simulators for coupled thermal-hydraulic-mechanical processes. Results from the two simulators are in very good agreement. The comparison between measurements and numerical results is also very satisfactory, regarding temperature, drift closure and rock deformation. Concerning backfill compaction, a parameter calibration through inverse modeling was performed due to insufficient data on crushed salt reconsolidation, particularly at high temperatures. We conclude that the two simulators investigated have the capabilities to reproduce the data available, which increases confidence in their use to reliably investigate disposal of heat-generating nuclear waste in saliferous geosystems.

58 GEOSCIENCES↗

DOE OSTI2018

A Gain Scheduling Wide-Area Damping Controller for the Efficient Integration of Photovoltaic Plant

With photovoltaic generation increasing, the wide-area oscillation has a vital influence on the long-distance power transmission of photovoltaic power. For an efficient integration of photovoltaic generation, a gain scheduling wide-area damping controller (WADC) is designed to guarantee system damping performance under a wide-area signal delay and a changing operating condition, which could be aggravated by the fluctuant and uncertain photovoltaic generation. A linear parameter-varying (LPV) model is proposed to represent all possible operating conditions within varying ranges of the photovoltaic generation and the load. Then, a tensor product (TP) model transformation is applied to transform the LPV model into a TP model. Based on the TP model, a parallel distributed compensation controller (PDC) framework is adopted to design the gain scheduling WADC, which is solved by linear matrix inequalities (LMIs) considering the accommodation to the random interval delay. In addition, a staircase basis function method is proposed to reduce the conservatism of LMIs within the PDC framework. The gain of the proposed WADC will be adjusted according to the operating condition acknowledged through the real-time scheduling and control center, once a wide-area oscillation is detected. Lastly, the performance of the proposed WADC is verified by a 2-area 4-machine system and a 68-bus 16-machine system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗

NASA NTRS2011

Modeling Weather Impact on Ground Delay Programs

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

Wang, Yao↗

DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1981

The effect of surface boundary conditions on the climate generated by a coarse-mesh general circulation model

A hierarchy of experiments was run, starting with an all water planet with zonally symmetric sea surface temperatures, then adding, one at a time, flat continents, mountains, surface physics, and realistic sea surface temperatures. The model was run with the sun fixed at a perpetual January. Ensemble means and standard deviations were computed and the t-test was used to determine the statistical significance of the results. The addition of realistic surface physics does not affect the model climatology to as large as extent as does the addition of mountains. Departures from zonal symmetry of the SST field result in a better simulation of the real atmosphere.

Cohen, C.↗

DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗